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作者： Gu, Jia-Wen Ching, Wai-Ki Siu, Tak-Kuen Zheng, Harry University of Hong Kong Advanced Modeling and Applied Computing Laboratory Department of Mathematics Pokfulam Road Hong Kong City University London London United Kingdom Macquarie University Sydney Australia Imperial College London United Kingdom

Corporate defaults may be triggered by some major market news or events such as financial crises or collapses of major banks or financial institutions. With a view to develop a more realistic model for credit risk analysis, we introduce a new type of reduced-form intensity-based model that can incorporate the impacts of both observable 'trigger' events and economic environment on corporate defaults. The key idea of the model is to augment a Cox process with 'trigger' events. Both single-default and multiple-default cases are considered in this paper. In the former case, a simple expression for the distribution of the default time is obtained. Applications of the proposed model to price defaultable bonds and multi-name Credit Default Swaps are provided. © 2014 Operational Research Society Ltd. All rights reserved.

关键词： Risk assessment

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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On modeling credit defaults: A probabilistic Boolean network approach

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Risk and Decision Analysis 2013年第2期4卷 119-129页

作者： Gu, Jia-Wen Siu, Tak-Kuen Zheng, Harry Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Pokfulam Road Hong Kong Department of Actuarial Studies Center for Financial Risk Macquarie University Sydney NSW Australia Department of Mathematics Imperial College London United Kingdom

One of the central issues in credit risk measurement and management is modeling and predicting correlated defaults. In this paper we introduce a novel model to investigate the relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults using the Probabilistic Boolean Network (PBN). The key idea of the PBN is to decompose a transition probability matrix describing correlated defaults of different sectors into several BN matrices which contain information about business cycles. An efficient estimation method based on an entropy approach is used to estimate the model parameters. Using real default data, we build a PBN for explaining the default structure and making reasonably good predictions of joint defaults in different sectors. © 2013-IOS Press and the authors.

关键词： Business cycles correlated defaults entropy probabilistic Boolean networks

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Credit portfolio management using two-level particle swarm optimization

Credit portfolio management using two-level particle swarm o...

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作者： Lu, Fu-Qiang Huang, Min Ching, Wai-Ki Siu, Tak Kuen Northeastern University at Qinhuangdao Qinhuangdao 066004 China Shenyang Liaoning 110819 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Department of Actuarial Studies Faculty of Business and Economics Macquarie University Sydney NSW 2109 Australia

In this paper, we propose a novel Two-level Particle Swarm Optimization (TLPSO) to solve the credit portfolio management problem. A two-date credit portfolio management model is considered. The objective of the manager is to minimize the maximum expected loss of the portfolio subject to a given consulting budget constraint. The captured problem is very challenging due to its hierarchical structure and its time complexity, so the TLPSO is designed for the credit portfolio management model. The TLPSO has two searching processes, namely, "internal-search", the searching process of the maximization problem and "external-search", the searching process of the minimization problem. The performance of TLPSO is then compared with both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), in terms of efficient frontiers, fitness values, convergence rates, computational time consumption and reliability. The experiment results show that TLPSO is more efficient and reliable for the credit portfolio management problem than the other tested methods. © 2013 Elsevier Inc. All rights reserved.

关键词： Genetic algorithms

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modeling GENETIC REGULATORY NETWORKS:A DELAY DISCRETE DYNAMICAL MODEL APPROACH

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Journal of Systems Science & Complexity 2012年第6期25卷 1052-1067页

作者： Hao JIANG Wai-Ki CHING Kiyoko F.AOKI-KINOSHITA Dianjing GUO Advanced Modeling and Applied Computing Laboratory Department of MathematicsThe University of Hong Kong Department of Bioinformatics Faculty of EngineeringSoka University Department of Biology The Chinese University of Hong Kong

modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.

关键词： Delay effect discrete dynamic system model genetic regulatory networks k-means clustering method linear multiple regression.

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The role of Eigen-matrix translation in classification of biological datasets

The role of Eigen-matrix translation in classification of bi...

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2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012

作者： Jiang, Hao Ching, Wai-Ki Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781467325585

Driven by the challenge of integrating large amount of experimental data obtained from biological research, computational biology and bioinformatics are growing rapidly. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular tools. In the perspective of kernel matrix, a technique namely Eigen-matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy owns a lot of nice properties while the nature of which needs further exploration. We propose that its importance lies in the dimension reduction of predictor attributes within the data set. This can therefore serve as a novel perspective for future research in dimension reduction problems. © 2012 IEEE.

关键词： Classification (of information)

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The role of Eigen-matrix translation in classification of biological datasets

The role of Eigen-matrix translation in classification of bi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Jiang Wai-Ki Ching Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong China

关键词： Kernel Proteins Protein engineering Support vector machines Accuracy Bioinformatics

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Optimal submission problem in a limit order book with var constraints

Optimal submission problem in a limit order book with var co...

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2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012

作者： Song, Na Ching, Wai-Ki Siu, Tak-Kuen Yiu, Cedric Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Hong Kong Hong Kong Department of Applied Finance and Actuarial Studies Center for Financial Risk Macquarie University Sydney NSW 2109 Australia Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9780769546902

We consider an optimal selection problem for bid and ask quotes subject to a value-at-Risk (VaR) constraint when arrivals of the buy and sell orders are governed by a Poisson process. The problem is formulated as a constrained utility maximization problem over a finite time horizon. Using a diffusion approximation to Poisson arrivals of market orders, the dynamic programming principle can be applied here. We propose an efficient procedure to solve this constrained utility maximization problem based on a successive approximation algorithm. Numerical examples with and without the VaR constraint are used to illustrate the effect of the risk constraint on the dealer's choices. We also conduct numerical experiments to analyze the impacts of the risk constraint on dealer's terminal profit. © 2012 IEEE.

关键词： Approximation algorithms

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Metabolite biomarker discovery for metabolic diseases by flux analysis

Metabolite biomarker discovery for metabolic diseases by flu...

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2012 IEEE 6th International Conference on Systems Biology, ISB 2012

作者： Li, Limin Jiang, Hao Ching, Wai-Ki Vassiliadis, Vassilis S. Institute of Information and System Science Xi'an Jiaotong University Xi'an 710049 China Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong Department of Chemical Engineering and Biotechnology University of Cambridge Pembroke Street Cambridge United Kingdom

ISBN: (纸本)9781467343985

Metabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers. © 2012 IEEE.

关键词： Metabolites

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